Magister Curationis - MCur / Infections (including healthcare-associated infections) are one of the leading causes of neonatal morbidity and mortality, yet these deaths could be prevented by cost-effective interventions. Handwashing (HW) is crucial to preventing the spread of antimicrobial resistance and reducing healthcare-associated infections (HCAIs). However, healthcare workers' compliance with optimal practices remains low in most settings.The purpose of this study was to determine the compliance with HW among nurses and midwives caring for newborn babies at Rwamagana Health Facilities, and the extent to which demographic and cognitive characteristics predict nurses' HW compliance. The study’s methodology involved a cross-sectional approach encompassing descriptive and quantitative methods. The data was collected over a period of 3 weeks from a total sample of 139 nursemidwives. An anonymous self-administered questionnaire was used to collect data; out of 139 questionnaires distributed 134 were returned back completed, giving a rate of 96.4%. The data analysis was done using SPSS software version 21.The results demonstrate that a mean self-reported HW compliance rate was 82.00% (SD= 13.60). A compliance rate of 80% or greater was confirmed by most of the participants in the study (79.1%). The highest reported rate of hand washing was HW after exposure to the newborn's body fluids (M= 89.33%; SD= 14.878),while results revealed that nurses tend to perform less HW after touching an object in the vicinity of the patient(73.43%; SD=22.81). A multiple regression analysis revealed that attitudes, subjective norms and intentions were unique independent predictors of HW compliance. However, results did not show any relationship between HW and nurses' demographic characteristics.This study recommends that, strategies aiming to improve HW compliance must be focused on concepts that predict HW. Further research ought to be conducted using observational approach.

Clostridium difficile infection (CDI) is the leading cause of bacterial infectious diarrhea in nosocomial settings and approximately 25% of patients with CDI experience disease recurrence. Prior CDI epidemiological investigations are limited though. They do not reflect the burden of CDI in federal facilities, nor do they capture recent estimates on patient health outcomes. Furthermore, few studies have integrated CDI recurrence risk factors into a tool that clinicians can use to identify patients at risk for CDI recurrence. This study 1) described the epidemiology of CDI in the national Veterans Health Administration (VHA), 2) derived and validated a clinical prediction rule for 60-day CDI first recurrence, and 3) derived and validated a clinical prediction rule for 60-day CDI second recurrence. This was a retrospective cohort study of VHA beneficiaries with CDI between October 1, 2001 and September 30, 2012. VHA clinical and pharmacy data were integrated to develop several independent variables, including patient baseline demographics, CDI characteristics, comorbidities, concomitant medications, prior medications, prior hospitalization, hospital length of stay (LOS), and CDI severity. The dependent variables included 30/60/90-day mortality, and 30/60/90-day CDI recurrence. CDI incidence and outcomes were presented descriptively and compared using generalized linear regression models. CDI recurrence prediction rules were derived using multivariable logistic regression models and validated using the area under the receiver-operating-characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. Our study demonstrated that CDI first episodes, recurrences, and severity increased over the study period, while mortality decreased. Our CDI first recurrence prediction rule included the following predictor variables: dyslipidemia, CDI type, renal disease, hospital LOS <7 days, principal CDI diagnosis, concomitant gastric acid suppressors, and concomitant antibiotics. This model demonstrated moderate 60-day first recurrence discrimination (AUROC=0.62). Our CDI second recurrence prediction rule was similar in predictor variables and validity. In conclusion, CDI is an important, rapidly-emerging public health problem in the VHA. A clinical prediction rule might aid clinicians in directing preventative therapies to patients at high risk for CDI recurrence. / text

Nosocomial infections are a cause of concern for hospital patients and the incidence rates of these infections are greater in intensive care units (ICUs) due to the invasive nature of treatments, additional risk factors and comorbidities, and therapies used. Invasive devices, such as vascular central lines, Foley catheters, and mechanical ventilators pose a risk for critically ill patients in the ICUs to develop device-related, healthcare-associated infections (HAI). The purpose of this study was to describe the epidemiological characteristics of patients who developed device-related HAIs within 3 ICU units (medical-surgical, cardiovascular, and neurosurgical) of an academic medical facility. The ecosocial theory of disease distribution provided the theoretical framework for the study to describe how ecological and social determinants interact and affect health variances. Secondary data were analyzed using analysis of variance (ANOVA), Pearson correlations, and chi-square statistical tests. A total of 4,213 patients admitted to the 3 ICUs from 2010-2014 were identified. According to the chi-square analysis, there was significant association between race/ethnicity and type of device-associated infection; between gender and types of infection; and between risk factors (diabetes, obesity, smoking habits) and kinds of infection, all of which the statistical significance had varied for each individual ICU. Bacterial differences were noted between device-associated infections. The potential positive social change from this study could be insight on possible new processes and interventions to reduce nosocomial infections and improve adult ICU patient outcomes such as decreased HAIs, decreased length of stay, comorbidities, and cost for both the patient and the hospital.

Background Clostridium difficile is a Gram-positive bacteria found in the large bowel or colon that causes mild to severe intestinal conditions and sometimes death. The primary risk factors for development of Clostridium difficile infection (CDI) include healthcare exposure and recent antimicrobial use. The purpose of this study is to compare risk factors associated with CDI occurring in the Community to those associated with Healthcare Facility Associated CDI in the metro Atlanta population from September 1, 2009 – April 30, 2011. Methods Patients were identified through C. difficile surveillance program of the Georgia Emerging Infections Program (EIP). Prospective, population based, laboratory based surveillance for all positive C. difficile cases in the Georgia Health District 3 (HD3). Due to the sampling scheme, for this analysis CO-HCFA and HCFO cases were combined to make a Healthcare Facility Associated (HCFA) classification. Using SAS, a logistic regression analysis was performed to compare the associated risks between CA and HCFA classifications. Results The rate of CDI in the HD3 counties in Georgia is 84 per 100,000. The median age of infection is 63 and the age range in this study is 1 to 102 years old. CA cases represented 38% of the sampled population. CDI cases 65 and older were more likely to have a Healthcare association compared to CA-CDI cases (p Conclusion This study supports literature about CDI and antimicrobial use and looks further in to the role underlying conditions play as a risk factor for HCFA-CDI cases.

Healthcare-associated infections occur commonly in hospitals and have a major impact on patient well-being. The occurrence of the healthcare-associated infection, Clostridium difficile, has been occurring more frequently among hospitalized patients due to an epidemic strain, and is an important cause of antibiotic-associated diarrhea and colitis. This study examined the impact of several organizational factors on the occurrence of C. difficile infection (CDI) in hospitals using an institutional theory perspective. Administrative claims were utilized from University HealthSystem Consortium hospitals to obtain hospital-level data for the calendar year 2011. Data were available for 89 hospitals. Hospital-level analyses, negative binomial regression models, were conducted to test eight developed hypotheses and the associations between organizational factors and the incidence of CDI in hospitals. Cases of CDI were risk-adjusted for known factors associated with CDI. After controlling for factors known to be associated with CDI, the results of the analyses showed that one study hypothesis was supported. That is, hospitals with higher Leapfrog Group Safety Scores had CDI rates that were no different than hospitals with lower Safety Scores. Further, it was found that U.S. News and World Report Best Hospital Honor Roll member hospitals had significantly higher occurrence of CDI as compared to non-Honor Roll member hospitals, though it was predicted that there would be no difference in CDI rates. The organizational factors state-led CDI prevention collaboratives, state mandatory CDI reporting, Magnet status, the rate of central line-associated bloodstream infections and catheter-associated urinary tract infections, and CDI physician champions, were not significantly associated with CDI occurrence.

Healthcare-associated infections is the most common healthcare related injury and affect almost every tenth patient. With the purpose of reducing these infections Infektionsverktyget, The Anti-Infection Tool, was developed for registration and feedback of infection data. The tool is now used in all Swedish county councils resulting in a wealth of data. The purpose of this thesis was thus to investigate how data mining can be applied to describe patterns in this data and predict patient outcomes regarding healthcare-associated infections that need to be treated with antibiotics. Data mining was performed with Microsoft SQL Server 2008 in which models based on six different data mining algorithms with different parameter settings were developed. They used the attributes gender, age and previous diagnoses and medical actions as inputs and antibiotic treated healthcare-associated infection outcome as output. The predictive performance of the models was evaluated using 5-fold cross validation and macro averaged measures of recall, precision and F-measure. Patterns generated by selected models were extracted. Models based on the Naive Bayes algorithm showed the highest predictive capabilities with respect to recall and models based on the Decision Trees algorithm with low pruning had the highest precision. Although, none were considered to perform sufficiently well and several areas of improvement were identified. The most important factor in the inadequate performance is believed to be the relatively rare occurrences of infections in the dataset. Extracted patterns based on the Association Rules algorithm were considered the easiest to interpret. Patterns included clinically valid and invalid as well as trivial relationships. Future studies should be focused on further model improvements and gathering of more patient data. The idea is that data mining in Infektionsverktyget in the future could be used both to provide ideas for further medical research and to identify risk patients and prevent healthcare-associated infections in daily clinical work. / Vårdrelaterade infektioner är den vanligaste vårdskadan och drabbar nästan var tionde patient. Med syfte att minska antalet vårdrelaterade infektioner utvecklades Infektionsverktyget för registrering och återkoppling av infektionsdata. Verktyget används nu i alla Sveriges landsting vilket resulterar i stora mängder data. Syftet med detta examensarbete var därför att undersöka hur data mining kan användas för att beskriva mönster i denna data och för att förutsäga om patienter kommer att drabbas av en vårdrelaterad infektion som behöver antibiotikabehandlas. Data mining genomfördes med Microsoft SQL Server 2008 där modeller baserade på sex olika data mining-algoritmer med olika parameterinställningar utvecklades. De hade inputattributen kön, ålder och tidigare diagnoser och medicinska åtgärder, och outputattributet utfall av antibiotikabehandlad vårdrelaterad infektion. Förutsägelseförmågan hos modellerna utvärderades med 5-delad korsvalidering och makrogenomsnitt av måtten recall, precision och F-measure. Fyra modeller användes även för att ta fram mönster ur datamängden. Modeller baserade på Naive Bayes-algoritmen hade den bästa förutsägelseförmågan med avseende på recall och modeller baserade på Decision Trees-algoritmen med en låg beskärningsnivå uppnådde bäst precision. Trots detta ansågs ingen av modellerna prestera tillräckligt bra och flera möjliga förbättringsområden hittades. Den viktigaste anledningen till den otillräckliga förutsägelseförmågan tros vara att infektioner är relativt ovanliga i datamängden. Mönster som tagits fram med Association Rules-algoritmen ansågs vara lättast att tolka. Mönstren innehöll både kliniskt relevanta och irrelevanta såväl som triviala samband. Framtida studier bör fokuseras på att förbättra modellerna ytterligare och att samla in mer patientdata. Idén är att data mining i Infektionsverktyget i framtiden skulle kunna användas för att ge uppslag till medicinsk forskning och för att identifiera riskpatienter och därmed förebygga vårdrelaterade infektioner i den dagliga kliniska verksamheten.

Medical implants constitute an essential advancement in modern medicine, often restoring or replacing functionality to failed organs. Whether a medical implant is temporary or permanent, medical implants carry the risk of implant failure due to an infection. Implant-associated infections (IAI) are challenging to treat and often result in increased medical costs, prolonged hospital stays, implant failure, and, in some instances, severe infections that can lead to amputations, sepsis, or mortality. Eradicating an IAI can be challenging since bacteria can form biofilms on the implant’s surface. The biofilms comprise an extracellular matrix protecting the bacterial cells against systemic antibiotics and the host’s immune system. Treating an IAI usually entails a broad range of antibiotic treatment and surgical procedures for tissue debridement or implant replacement. For the reasons stated above, scientists and engineers continue to develop technologies to protect the surface of medical implants against infections. Amongst the new technologies, Liquid-Infused Surfaces (LIS) are renowned for their repellent and anti-fouling properties created by tethering a stable liquid layer onto the surface. However, many challenges remain to adopt this technology for implantable devices. For instance, the high repellent properties can hinder implant-tissue interaction and discourage proper integration with the body. Furthermore, the stable liquid layer is contingent on the surface properties of the coated material. In other words, the long-term stability of these coatings may be compromised if the surface chemistry is covered by biological processes such as biofilm formation from adherent bacteria. This thesis aims to expand on the applications of LIS coatings and enhance their properties for implantable materials. This thesis reviews different types of antibiotic surface coatings and further examines LIS technologies as a viable antibacterial coating for medical implants. Then, three novel multifunctional LIS coatings are presented. The first developed coating enhanced the antibacterial properties of the coating by adding bactericidal agents within the LIS coating. The developed antibiotic liquid-infused coating not only repelled bacteria but also lysed bacteria upon contact. The second coating was designed to promote tissue integration. This multifunctional coating promoted cell deposition and proliferation while remaining repellent toward bacteria, while the conventional LIS coating displayed poor cell availability. Lastly, a collagen-bacteriophage conjugated liquid-infused coating was developed to promote tissue integration while having a two-tier layer of antibacterial protection. This coating was tested in a mouse sepsis model and prevented mortality of all mice, with other groups as high as 90% mortality. These coatings constitute essential steppingstones to bring LIS technology to medical implants. / Dissertation / Doctor of Philosophy (PhD) / Implant-associated infections (IAI) remain a significant problem in modern medicine. IAIs are challenging to treat and often result in increased medical costs, prolonged hospital stays, implant failure, and, in some instances, severe infections that can lead to sepsis or mortality. For these reasons, new technologies have been developed to protect the surface of medical implants against infections. Amongst the new technologies, Liquid-Infused Surfaces (LIS) are renowned for their repellent and anti-fouling properties created by tethering a stable liquid layer onto the surface. This thesis aims to expand on the applications of LIS coatings and enhance their properties for implantable materials. This thesis reviews different types of antibiotic surface coatings, examines LIS technologies, and presents three novel multifunctional LIS coatings. The newly developed coatings enhance the LIS coatings through the addition of antibacterial properties and biomolecules to promote tissue integration.

Bakgrund: Vårdrelaterade infektioner (VRI) ses som en av de främsta vårdskadorna i världen och beskrivs ge ett enormt lidande för patienten. Basala hygienrutiner började utvecklas redan på 1800-talet och är idag den huvudsakliga åtgärden för att minska VRI. Sjuksköterskor och övrig vårdpersonal har en skyldighet att följa lagar, författningar och riktlinjer så att omvårdnaden av patienter blir patientsäker. Syfte: Var att undersöka vårdpersonalens följsamhet till basala hygienrutiner. Metod: En kvantitativ retrospektiv longitudinell studie som byggde på observationer av vårdpersonal samt punkprevalensmätningar av VRI. Resultat: Det sågs en högre följsamhet till basala hygienrutiner hos vårdpersonalen på medicinkliniken. Medicinkliniken hade även en signifikant förbättring av följsamhet till basala hygienrutiner. Båda klinikerna hade en lägre följsamhet till desinfektion av händer före en patientkontakt än efteråt. Studerande hade högre följsamhet än sjuksköterskor och läkare. Medicinkliniken hade som mest 5 (n=43; 11,6%) patienter med en VRI och kirurgkliniken hade som mest 9 (n=63; 14,3%) patienter. Diskussion: Tidigare forskning visar att det är lägre följsamhet till desinfektion av händer före en patientkontakt än efteråt, något som även setts i resultatet. Tidigare forskning tar även upp möjliga orsaker till en låg följsamhet samt verktyg för att skapa en högre följsamhet till basala hygienrutiner / Background: Healthcare-associated infections (HAI) are one of the leading health damage in the world and described provides immense suffering to the patient. Basic hygiene began to develop early in the 1800s and is today the main arrangements for reducing HAI. Nurses and other health care workers has an obligation to comply with laws, regulations and guidelines so that the care of patients become safe. Objective: To investigate the health care workers’ adherence to basic hygiene routines. Method: A quantitative retrospective longitudinal study based on observations of health care workers and point prevalence surveys of HAI. Results: Showed a higher adherence to basic hygiene among health professionals in the medical clinic. Medical clinic had a significant improvement in adherence to basic hygiene. Both clinics had a lower adherence to hand disinfection before a patient contact than afterwards. Students had higher adherence than nurses and doctors. Medical clinic had at most 5 (n = 43, 11.6%) patients with a HAI and surgery clinic had at most 9 (n = 63, 14.3%) patients. Discussion: Past research shows that there is less adherence to hand disinfection before a patient contact then afterwards, which is also shown in the result. Previous research also mentions possible causes of a low adherence and tool to create a higher adherence to basic hygiene.

Low hand hygiene compliance among health staff is a problem globally in health care settings. Improving hand hygiene could prevent many of the health care associated infections that affects hundreds of millions of patients every year around the globe. The aim of this literature study was to describe obstacles to maintain proper hand hygiene in hospitals, seen from a global perspective. Eight articles from seven countries, all based on qualitative research have been analysed according to Friberg's five-step method that contributes to evidence-based nursing. The challenges to sustain hand hygiene found in the results were split into two main categories: Organizational factors and Individual factors. Organizational factors included five subcategories: Work environment, Inadequate education, Infrastructure, Insufficient management and Power structures. The results showed that when these factors were negatively affected so was compliance regarding proper hand hygiene. These were often issuesthat the health staff felt powerless to change and affect. Individual factors included two subcategories: Attitudes and social factors. Example of this could be healthcare workers neglected hygiene guidelines out of personal reasons or healthcare workers giving care to patients that didn’t accept how it was carried out because of social and cultural differences. The complex issues revolving these matters need to be addressed on an overall perspective. Cooperation between management and health staff is fundamental to obtain changes within healthcare organizations. Both parties need to work together to be able to improve hand hygiene compliance and to reduce healthcare associated infections.

Orientador: Carlos Magno Castelo Branco Fortaleza / Resumo: Estudos anteriores do nosso grupo reforçaram a evidência recente e inesperada de sazonalidade e determinação meteorológica na incidência das Infecções Relacionadas à Assistência à Saúde (IRAS, anteriormente chamadas “Infecções Hospitalares”. No entanto, nenhuma pesquisa até o momento associou esses agravos à temperatura e umidade nos diversos setores de um hospital. Com o objetivo de preencher esse hiato no conhecimento sobre epidemiologia das IRAS, realizamos um estudo ecológico baseado na avaliação de temperatura e umidade em áreas assistenciais. Resumidamente, oito termo-higrômetros foram afixados em diferentes unidades do Hospital das Clínicas da Faculdade de Medicina de Botucatu, e dois outros foram utilizados de forma móvel para os demais setores de internação desse mesmo hospital. Os resultados obtidos serão comparados com: (a) dados informados por estação meteorológica no município de Botucatu; (b) indicadores de incidência de IRAS; (c) Indicadores de incidência de microrganismos multidroga-resistentes. O estudo teve duração de 12 meses. A análise estatística envolveu modelos multivariados de regressão de Poisson e Regressão Logística. Os resultados demonstraram que, apesar de haver diferença significativa, temperatura e umidade no interior do hospital (mesmo em áreas climatizadas) variam em associação estatisticamente significante com os parâmetros medidos em estação meteorológica. Apesar do pouco tempo de observação, foi possível constatar associação entre temperatu... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Previous studies by our group have reinforced recent and unexpected evidence of seasonality and meteorological determination in the incidence of Healthcare-Associated Infections (HAI’s, formerly called “Nosocomial infections.") However, no research to date has associated these conditions with temperature and humidity. In order to fill this gap in the knowledge on HAI epidemiology, we carried out an ecological study based on the evaluation of temperature and humidity in care areas. In summary, eight thermohygrometers were posted in different units of the teaching hospital of Botucatu Medical School (“Hospital das Clínicas da Faculdade de Medicina de Botucatu”). The results obtained would be compared with: (a) data reported by meteorological station in the city of Botucatu, (b) incidence of HAI; (c) incidence of multidrug-resistant microorganisms. The study lasted 12 months. Statistical analysis involved multivariate Poisson regression and logistic regression models. The results showed that, although there is a significant difference, temperature and humidity inside the hospital (even in climatized areas) vary in a statistically significant association with the parameters measured in meteorological station. Despite the short observation period, it was possible to verify the association between temperature and HAI (including multidrug-resistant microorganisms) in the Intensive Care Unit (ICU), wards for non-critically ill patients and Surgical Theater. Taken together, our findin... (Complete abstract click electronic access below) / Mestre